{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Self-optimizing ML: A simple case illustration\n",
    "\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "In this demo, we show the core idea of a self-optimizing ML modeling: Running an optimization loop wrapped over a ML algorithm to ***optimally meet a target of not only the ML performance but something else*** - a business goal, a computational cost burden, a complexity measure, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = datasets.make_hastie_10_2(n_samples=20000, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.624345</td>\n",
       "      <td>-0.611756</td>\n",
       "      <td>-0.528172</td>\n",
       "      <td>-1.072969</td>\n",
       "      <td>0.865408</td>\n",
       "      <td>-2.301539</td>\n",
       "      <td>1.744812</td>\n",
       "      <td>-0.761207</td>\n",
       "      <td>0.319039</td>\n",
       "      <td>-0.249370</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.462108</td>\n",
       "      <td>-2.060141</td>\n",
       "      <td>-0.322417</td>\n",
       "      <td>-0.384054</td>\n",
       "      <td>1.133769</td>\n",
       "      <td>-1.099891</td>\n",
       "      <td>-0.172428</td>\n",
       "      <td>-0.877858</td>\n",
       "      <td>0.042214</td>\n",
       "      <td>0.582815</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.100619</td>\n",
       "      <td>1.144724</td>\n",
       "      <td>0.901591</td>\n",
       "      <td>0.502494</td>\n",
       "      <td>0.900856</td>\n",
       "      <td>-0.683728</td>\n",
       "      <td>-0.122890</td>\n",
       "      <td>-0.935769</td>\n",
       "      <td>-0.267888</td>\n",
       "      <td>0.530355</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.691661</td>\n",
       "      <td>-0.396754</td>\n",
       "      <td>-0.687173</td>\n",
       "      <td>-0.845206</td>\n",
       "      <td>-0.671246</td>\n",
       "      <td>-0.012665</td>\n",
       "      <td>-1.117310</td>\n",
       "      <td>0.234416</td>\n",
       "      <td>1.659802</td>\n",
       "      <td>0.742044</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.191836</td>\n",
       "      <td>-0.887629</td>\n",
       "      <td>-0.747158</td>\n",
       "      <td>1.692455</td>\n",
       "      <td>0.050808</td>\n",
       "      <td>-0.636996</td>\n",
       "      <td>0.190915</td>\n",
       "      <td>2.100255</td>\n",
       "      <td>0.120159</td>\n",
       "      <td>0.617203</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2        X3        X4        X5        X6        X7  \\\n",
       "0  1.624345 -0.611756 -0.528172 -1.072969  0.865408 -2.301539  1.744812   \n",
       "1  1.462108 -2.060141 -0.322417 -0.384054  1.133769 -1.099891 -0.172428   \n",
       "2 -1.100619  1.144724  0.901591  0.502494  0.900856 -0.683728 -0.122890   \n",
       "3 -0.691661 -0.396754 -0.687173 -0.845206 -0.671246 -0.012665 -1.117310   \n",
       "4 -0.191836 -0.887629 -0.747158  1.692455  0.050808 -0.636996  0.190915   \n",
       "\n",
       "         X8        X9       X10    y  \n",
       "0 -0.761207  0.319039 -0.249370  1.0  \n",
       "1 -0.877858  0.042214  0.582815  1.0  \n",
       "2 -0.935769 -0.267888  0.530355 -1.0  \n",
       "3  0.234416  1.659802  0.742044 -1.0  \n",
       "4  2.100255  0.120159  0.617203  1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data=X,columns=['X'+str(i) for i in range(1,11)])\n",
    "df['y']=pd.Series(y)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=1\n",
    "plt.figure(figsize=(20,20))\n",
    "for c in df.columns[:-1]:\n",
    "    plt.subplot(4,3,i)\n",
    "    plt.title(f\"Boxplot of {c}\",fontsize=16)\n",
    "    plt.yticks(fontsize=12)\n",
    "    plt.xticks(fontsize=12)\n",
    "    sns.boxplot(y=df[c],x=df['y'])\n",
    "    i+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.725x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sample=df.sample(frac=0.01)\n",
    "sns.set(style=\"ticks\")\n",
    "g=sns.pairplot(df_sample,vars=[\"X1\",\"X2\",\"X3\"],\n",
    "               hue=\"y\",markers=[\"o\", \"s\"],\n",
    "               diag_kind=\"kde\",diag_kws=dict(shade=True),plot_kws=dict(s=100,alpha=0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation and test/train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('y',axis=1)\n",
    "y=df['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of validation set: (3000, 10)\n",
      "Shape of test set: (3000, 10)\n",
      "Shape of training set: (14000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of validation set:\", X_val.shape)\n",
    "print(\"Shape of test set:\", X_test.shape)\n",
    "print(\"Shape of training set:\", X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification algorithms and the metrics - `accuracy_score` and `f1_score`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the classification over a range of trees in the `AdaBoost` meta-estimator and record accuracy and compute time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done for number of trees: 1\n",
      "Done for number of trees: 2\n",
      "Done for number of trees: 3\n",
      "Done for number of trees: 4\n",
      "Done for number of trees: 5\n",
      "Done for number of trees: 6\n",
      "Done for number of trees: 7\n",
      "Done for number of trees: 8\n",
      "Done for number of trees: 9\n",
      "Done for number of trees: 10\n",
      "Done for number of trees: 11\n",
      "Done for number of trees: 12\n",
      "Done for number of trees: 13\n",
      "Done for number of trees: 14\n",
      "Done for number of trees: 15\n",
      "Done for number of trees: 16\n",
      "Done for number of trees: 17\n",
      "Done for number of trees: 18\n",
      "Done for number of trees: 19\n",
      "Done for number of trees: 20\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "val_acc_num_trees=[]\n",
    "val_f1_num_trees=[]\n",
    "train_acc_num_trees=[]\n",
    "train_f1_num_trees=[]\n",
    "time_adaboost=[]\n",
    "val_range=(1,21,1)\n",
    "for i in range(val_range[0],val_range[1],val_range[2]):\n",
    "    t1=time.time()\n",
    "    # Fitting\n",
    "    adaboost = AdaBoostClassifier(DecisionTreeClassifier(min_samples_leaf=20,max_depth=2),\n",
    "                            n_estimators=i,learning_rate=0.2)\n",
    "    adaboost.fit(X_train,y_train)\n",
    "    pred_train = adaboost.predict(X_train)\n",
    "    pred_val = adaboost.predict(X_val)\n",
    "    # Accuracy and F1 score\n",
    "    acc_train=accuracy_score(y_train,pred_train)\n",
    "    f1_train = f1_score(y_train,pred_train,average='micro')\n",
    "    acc_val=accuracy_score(y_val,pred_val)\n",
    "    f1_val = f1_score(y_val,pred_val,average='micro')\n",
    "    # Appending to the lists\n",
    "    train_acc_num_trees.append(acc_train)\n",
    "    train_f1_num_trees.append(f1_train)\n",
    "    val_acc_num_trees.append(acc_val)\n",
    "    val_f1_num_trees.append(f1_val)\n",
    "    t2=time.time()\n",
    "    time_adaboost.append(t2-t1)\n",
    "    print(f\"Done for number of trees: {i}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of accuracy and compute time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(val_range[0],val_range[1],val_range[2]),train_acc_num_trees,c='red')\n",
    "plt.plot(range(val_range[0],val_range[1],val_range[2]),val_acc_num_trees,c='blue')\n",
    "plt.legend([\"Training\",\"Validation\"],fontsize=15)\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel(\"Number of trees in the meta-estimator\", fontsize=16)\n",
    "plt.ylabel(\"Accuracy\",fontsize=16)\n",
    "plt.ylim(0.5,1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(val_range[0],val_range[1],val_range[2]),time_adaboost,c='red')\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel(\"Number of trees in the meta-estimator\", fontsize=16)\n",
    "plt.ylabel(\"Model training time (seconds)\",fontsize=16)\n",
    "#plt.ylim(0.7,1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A simple linear objective function for the business-centric optimization\n",
    "\n",
    "The optimization (objective function) looks like following,\n",
    "\n",
    "$$ \\textbf{objective} = \\alpha.\\frac{v-v_{th}}{v_{max}}+\\beta.\\frac{t}{t_{max}} $$\n",
    "\n",
    "Here, $v$ denotes the validation set accuracy and $t$ denotes the compute time. Note the use of $v_{th}$ as a threshold value only above which the accuracy matters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = np.array(val_acc_num_trees)\n",
    "t = np.array(time_adaboost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_th = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "v=(v-v_th)/v.max()\n",
    "t=t/t.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 3\n",
    "beta = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "o = alpha*v-beta*(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "plt.plot(range(val_range[0],val_range[1],val_range[2]),o,color='k',marker='o',lw=3,markersize='10')\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel(\"Number of trees in the meta-estimator\", fontsize=16)\n",
    "plt.ylabel(\"Linear objective function\",fontsize=16)\n",
    "plt.xticks([i for i in range(2,21,2)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing the objective function formally for Scipy run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(x, v_th=0.5,alpha=3,beta=1):\n",
    "    \"\"\"\n",
    "    Objective function computing a overall cost function\n",
    "    involving the running time and performance of the AdaBoost classifier fitting\n",
    "    x: number of trees to be used by the meta-estimator (AdaBoost)\n",
    "    V_th: Minimum accuracy threshold\n",
    "    alpha: Cost factor of the accuracy (indicator of profit)\n",
    "    beta: Cost factor of running time (indicator of expense)\n",
    "    \"\"\" \n",
    "    x = int(x)\n",
    "    if x<1:\n",
    "        x=1\n",
    "    t1=time.time()\n",
    "    # Fitting\n",
    "    adaboost = AdaBoostClassifier(DecisionTreeClassifier(min_samples_leaf=20,max_depth=2),\n",
    "                            n_estimators=x,learning_rate=0.2)\n",
    "    adaboost.fit(X_train,y_train)\n",
    "    t2=time.time()\n",
    "    pred_train = adaboost.predict(X_train)\n",
    "    pred_val = adaboost.predict(X_val)\n",
    "    # Accuracy and F1 score\n",
    "    acc_train=accuracy_score(y_train,pred_train)\n",
    "    f1_train = f1_score(y_train,pred_train,average='micro')\n",
    "    acc_val=accuracy_score(y_val,pred_val)\n",
    "    f1_val = f1_score(y_val,pred_val,average='micro')\n",
    "    \n",
    "    v = acc_val # Validation set accuracy\n",
    "    t = t2-t1 # Time taken for fitting and calculating the accuracy\n",
    "    \n",
    "    # Normalize the accuracy and time measures\n",
    "    #v=(v-v_th)/v.max()\n",
    "    #t=t/t.max()\n",
    "    \n",
    "    # Objective function (a profit measure)\n",
    "    obj = alpha*v-beta*(t)\n",
    "    \n",
    "    return -float(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.898426469167074, -3.140829397837321, -3.3419099394480387, -3.392640603383382, -3.4783966541290283, -3.5320983759562172, -3.653601512908936, -3.7516391181945803, -3.805677728652954, -3.8654566160837813, -3.7641517766316728, -3.712255900700887, -3.750063428878784, -3.754453824361166, -3.752184219360352, -3.7502264690399167, -3.695710490544637, -3.6637176926930746, -3.6569201628367107, -3.717943604787191, "
     ]
    }
   ],
   "source": [
    "for i in range(1,21):\n",
    "    print(objective(i,0.5,5,1),end=', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=np.arange(1,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n,list(map(objective,n)),color='k',marker='o',lw=3,markersize='10')\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xlabel(\"Number of trees in the meta-estimator\", fontsize=16)\n",
    "plt.ylabel(\"Linear objective function\",fontsize=16)\n",
    "plt.xticks([i for i in range(2,21,2)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling `minimize_scalar` from Scipy\n",
    "\n",
    "For a detailed discussion on Scipy optimization functions see this article on Medium: \n",
    "\n",
    "[\"Optimization with SciPy and application ideas to machine learning\"](https://towardsdatascience.com/optimization-with-scipy-and-application-ideas-to-machine-learning-81d39c7938b8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Optimization terminated successfully;\n",
      "The returned value satisfies the termination criteria\n",
      "(using xtol =  1e-05 )\n"
     ]
    }
   ],
   "source": [
    "r=minimize_scalar(objective,bounds=(2,21),options={'disp':True},method='Bounded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('fun', -2.281078157424927), ('status', 0), ('success', True), ('message', 'Solution found.'), ('x', 9.764352625570279), ('nfev', 16)])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For various datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective_dataset(x, X_train,y_train,X_val,y_val,v_th=0.5,alpha=3,beta=1):\n",
    "    \"\"\"\n",
    "    Objective function computing a overall cost function\n",
    "    involving the running time and performance of the AdaBoost classifier fitting\n",
    "    x: number of trees to be used by the meta-estimator (AdaBoost)\n",
    "    V_th: Minimum accuracy threshold\n",
    "    alpha: Cost factor of the accuracy (indicator of profit)\n",
    "    beta: Cost factor of running time (indicator of expense)\n",
    "    \"\"\" \n",
    "    x = int(x)\n",
    "    if x<1:\n",
    "        x=1\n",
    "    t1=time.time()\n",
    "    # Fitting\n",
    "    adaboost = AdaBoostClassifier(DecisionTreeClassifier(min_samples_leaf=20,max_depth=2),\n",
    "                            n_estimators=x,learning_rate=0.2)\n",
    "    adaboost.fit(X_train,y_train)\n",
    "    t2=time.time()\n",
    "    pred_train = adaboost.predict(X_train)\n",
    "    pred_val = adaboost.predict(X_val)\n",
    "    # Accuracy and F1 score\n",
    "    acc_train=accuracy_score(y_train,pred_train)\n",
    "    f1_train = f1_score(y_train,pred_train,average='micro')\n",
    "    acc_val=accuracy_score(y_val,pred_val)\n",
    "    f1_val = f1_score(y_val,pred_val,average='micro')\n",
    "    \n",
    "    v = acc_val # Validation set accuracy\n",
    "    t = t2-t1 # Time taken for fitting and calculating the accuracy\n",
    "    \n",
    "    # Normalize the accuracy and time measures\n",
    "    #v=(v-v_th)/v.max()\n",
    "    #t=t/t.max()\n",
    "    \n",
    "    # Objective function (a profit measure)\n",
    "    obj = alpha*v-beta*(t)\n",
    "    \n",
    "    return -float(obj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.047340010991547\n",
      "---------------\n",
      "17.998823385073866\n",
      "---------------\n",
      "17.673669979415227\n",
      "---------------\n",
      "18.039625249357616\n",
      "---------------\n",
      "14.44972126082691\n",
      "---------------\n"
     ]
    }
   ],
   "source": [
    "for _ in range(5):\n",
    "    X, y = datasets.make_hastie_10_2(n_samples=2000, random_state=1)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)\n",
    "    X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.50)\n",
    "    result=minimize_scalar(objective_dataset,bounds=(2,20),args=(X_train,y_train,X_val,y_val),method='Bounded')\n",
    "    print(result['x'])\n",
    "    print(\"-\"*15)"
   ]
  }
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